As flood intensities are increasing, Flood Early Warning Systems (FEWS) are becoming more crucial so the right mitigation measures can be taken. This study focused on the Phetchaburi river basin in Thailand, which experiences floods yearly. Currently, precipitation data from rain gauges are used in the FEWS for this region. However, weather radar is also available in Thailand. Weather radar is usually able to capture the spatial variability of precipitation in more detail. Therefore, this study aimed to research the effect of spatially distributed precipitation on flood modelling in the Phetchaburi river basin.

Using HEC-RAS, a hydrodynamic rainfall-runoff model of the Phetchaburi river basin was made. The area of interest was the middle reach of the Phetchaburi river and its two tributaries, lying in-between three reservoir dams upstream and the Phet Diversion Dam downstream. The Phetchaburi river and its tributaries were calibrated using measured dam outflow and water level data. Three types of precipitation data were used as input, namely homogeneous precipitation data and spatially distributed precipitation data obtained from weather radar and from rain gauges.

High infiltration rates were found for the Phetchaburi river basin. When homogeneous precipitation data was used as input, precipitation intensity would be too low, allowing all precipitation to infiltrate into the subsurface. Using homogenous precipitation data as input results in a underestimation of floods. On the contrary, precipitation intensities in spatially distributed precipitation data were high enough to exceed the soil infiltration capacity, leading to surface runoff and floods. When solely looking at the water balance, using precipitation data from weather radar and rain gauges as input lead to similar results. However, when it came to the water levels at specific locations, precipitation data from weather radar performed better. The density of the rain gauge network in the Phetchaburi river basin is too low to capture the spatial variability of the precipitation events in detail. This resulted in floods or the
lack thereof at the wrong locations. Weather radar captures the spatial variability of precipitation in greater detail than rain gauges can. This study showed that using precipitation data from weather radar as input results in more accurate flood modelling in the Phetchaburi river basin.

Landslides are one of the most widespread natural hazards, causing thousands of casualties every year and significant economic damage worldwide. In mountainous regions of Thailand, damages and fatalities due to landslides have increased over the last decades due to intensified human interventions.

This research aims to contribute to the development of a near real-time, joint early warning system for flash floods and landslides in Khao Yai National Park, Thailand. Spatial and temporal landslide occurrences in Khao Yai National Park were assessed. To extend the available landslide inventory, the detection of historical landslides was automated on the Google Earth Engine platform using remotely sensed relative changes in NDVI. Predisposing factors of landslide occurrence were determined using the frequency ratio method. A susceptibility map was then derived by linking the detected landslides to the predisposing factors: slope angle, aspect, land use, lithology, and distance to road. Validation with the landslide inventory shows excellent classification performance.

The temporal probability was assessed using a physically-based multi-hazard model. The hydrological model includes the driving hydrological processes for each grid cell. The streamflow model output was calibrated using observed nested river discharge events. The model performance was tested under spatially distributed precipitation forcing of varying quality. The results show good performance in predicting streamflow and landslide occurrence based on modeled antecedent conditions when using high-quality weather radar information as forcing.

This research demonstrates the effectiveness of integrating fully distributed physically-based modeling with high-resolution spatial rainfall observations from weather radar. The findings are expected to enhance the development of a multi-hazard early warning system and improve disaster risk management in northeastern Thailand.